DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-11are directed to a system, claims 12-20 are directed to a method,. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 12:
A method of identifying an ideal solvent mix for use in an industrial process, the method comprising:
inputting data on a plurality of solvents into a computer designed to run an artificial intelligence algorithm wherein the computer comprises the artificial intelligence algorithm, wherein the artificial intelligence algorithm is able to process the data to output useful information on the component mix (eutectic solvent);
running the algorithm to generate the useful information; and
evaluating the useful information to identify the ideal solvent mix.
Step 2A Prong 1:
“inputting data on a plurality of solvents into a computer designed to run an artificial intelligence algorithm wherein the computer comprises the artificial intelligence algorithm, wherein the artificial intelligence algorithm is able to process the data to output useful information on the component mix” is directed to mental step of data gathering and processing data.
“evaluating the useful information to identify the ideal solvent mix” is directed to mental step of analyzing data.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“inputting data on a plurality of solvents into a computer designed to run an artificial intelligence algorithm wherein the computer comprises the artificial intelligence algorithm, wherein the artificial intelligence algorithm is able to process the data to output useful information on the component mix” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“running the algorithm to generate the useful information” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“evaluating the useful information to identify the ideal solvent mix” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is merely collecting data, manipulating or analyzing the data using mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 12 recites the additional element(s) of using generic AI/ML technology, i.e. *** an artificial intelligence algorithm ***, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the *** artificial intelligence algorithm *** merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of *** artificial intelligence algorithm *** to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“inputting data on a plurality of solvents into a computer designed to run an artificial intelligence algorithm wherein the computer comprises the artificial intelligence algorithm, wherein the artificial intelligence algorithm is able to process the data to output useful information on the component mix” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“running the algorithm to generate the useful information” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“evaluating the useful information to identify the ideal solvent mix” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is therefore ineligible under 35 USC 101.
Claim 1 is similar to claim 12 but recites a system for identifying ideal solvents to use in an industrial process, the system comprising combining eutectic solvents with artificial intelligence. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 1 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 1.
Dependent claims 2-11 and 13-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below: there is no additional element(s) in the dependent claims that adds a meaningful limitation to the abstract idea to make the claim significantly more than the judicial exception (abstract idea).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-15 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by 'Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning' to CYSEWSKI, et al. (hereinafter 'Cysewski')(IDS record filed on 04/30/2025).
Regarding claim 1, Cysewski discloses a system for identifying ideal solvents to use in an industrial process, the system comprising combining eutectic solvents with artificial intelligence (Neural Networks SANNs machine learning protocol (artificial intelligence) was used in screening for natural deep eutectics solvents NADES with higher solubility of theophylline pharmaceutical ingredient (to use in an industrial process); abstract; page 2, second paragraph; page 15, second paragraph).
Regarding claim 2, Cysewski discloses the system of claim 1, and further discloses wherein the ideal solvent is a mixture of two or more components (binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water; abstract; page 5, first paragraph; page 12, third paragraph).
Regarding claim 3, Cysewski discloses the system of claim 2, and further discloses wherein the ideal solvent further comprises water as a component (binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water; abstract; page 5, first paragraph; page 12, third paragraph).
Regarding claim 4, Cysewski discloses the system of claim 1, and further discloses wherein the artificial intelligence uses an algorithm, said algorithm comprising one or more members selected from the group consisting of a linear regression algorithm, a logistic regression algorithm, a decision tree algorithm, a SVM algorithm, a Naive Bayes algorithm, a KNN algorithm, a K-means algorithm, a Random Forest algorithm (optional), a complex neural network algorithm (modeling was performed using ensemble of artificial neural networks, wherein algorithms utilizing multilayer perceptions were used during the machine learning phase (complex neural network algorithm); page 15, second paragraph), a support-vector machine algorithm, a gradient boosting algorithm, a DBSCAN algorithm, a dimensionally reduction algorithm, a gradient boosting algorithm, an AdaBoosting algorithm, and combinations thereof (optional).
Regarding claim 5, Cysewski discloses the system of claim 4, and further discloses wherein the algorithm is a Random Forest algorithm or a complex neural network algorithm (modeling was performed using ensemble of artificial neural networks, wherein algorithms utilizing multilayer perceptions were used during the machine learning phase (complex neural network algorithm); page 15, second paragraph).
Regarding claim 6, Cysewski discloses the system of claim 1, and further discloses wherein the identifying ideal solvents comprises replacing specific solvents or ingredients used in an industrial process with eutectic solvents (the highest solubility of theophylline was found in the NADES made of choline chloride and glycerol, which is 80% higher than pure DMSO that is currently used as a solvent for the APls (replacing specific solvent used in an industrial process); page 5, first paragraph; page 15, fourth paragraph).
Regarding claim 7, Cysewski discloses the system of claim 1, and further discloses wherein the identifying ideal solvents comprises identifying one or more physicochemical properties (the input layer comprised the sets of seven molecular descriptors depicting intermolecular interactions, and the output layer was the estimated solubility (physicochemical property); page 15, second paragraph).
Regarding claim 9, Cysewski discloses the system of claim 5, and further discloses wherein an accuracy for predicting the ideal solvents is at least about 80% (for SANN algorithm, the accuracy of the obtained model shows 4 outliers out of 160 binary and ternary NADES samples tested (97.5 % accuracy); page 8, first paragraph; page 9, first paragraph, figure 6, top).
Regarding claim 10, Cysewski discloses the system of claim 9, and further discloses wherein the accuracy for predicting the ideal solvents is at least about 90% (for SANN algorithm, the accuracy of the obtained model shows 4 outliers out of 160 binary and ternary NADES samples tested (97.5 % accuracy); page 8, first paragraph; page 9, first paragraph, figure 6, top).
Regarding claim 11, Cysewski discloses the system of claim 1, and further discloses wherein the ideal solvents is a mixture of two or more solvents , wherein the ideal solvents further comprise water as component (binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water; abstract; page 5, first paragraph; page 12, third paragraph), and wherein the artificial intelligence uses an algorithm, said algorithm comprising one or more members selected from the group consisting of a linear regression algorithm, a logistic regression algorithm, a decision tree algorithm, a SVM algorithm, a Naive Bayes algorithm, a KNN algorithm, a K-means 13 algorithm, a Random Forest algorithm (optional), a complex neural network algorithm (modeling was performed using ensemble of artificial neural networks, wherein algorithms utilizing multilayer perceptions were used during the machine learning phase (complex neural network algorithm); page 15, second paragraph), a support-vector machine algorithm, a gradient boosting algorithm, a DBSCAN algorithm, a dimensionally reduction algorithm, a gradient boosting algorithm, an AdaBoosting algorithm, and combinations thereof (optional).
Regarding claim 12, Cysewski discloses a method of identifying an ideal solvent mix for use in an industrial process (screening for natural deep eutectics solvents NADES with higher solubility of theophylline pharmaceutical ingredient (to use in an industrial process abstract; page 2, second paragraph; page 15, second paragraph), the method comprising: inputting data on a plurality of solvents into a computer designed to run an artificial intelligence algorithm wherein the computer comprises the artificial intelligence algorithm (machine learning protocol (artificial intelligence algorithm) included an input layer comprised of sets of seven molecular descriptors depicting intermolecular interactions of NADES (eutectic solvent); page 15, second paragraph), wherein the artificial intelligence algorithm is able to process the data to output useful information on the component mix (eutectic solvent); running the algorithm to generate the useful information (machine learning protocol (artificial intelligence algorithm) included an input layer comprised of sets of seven molecular descriptors depicting intermolecular interactions of NADES (eutectic solvent), and the output layer comprises theophylline solubility (useful information) in the NADES; abstract; page 15, second paragraph); and evaluating the useful information to identify the ideal solvent mix (the highest solubility of theophylline was found in the NADES made of choline chloride and glycerol, which is 80% higher than pure DMSO that is currently used as a solvent for the APls; page 5, first paragraph; page 15, fourth paragraph).
Regarding claim 13, Cysewski discloses the method of claim 12, and further discloses wherein the ideal solvent mix comprises at least one eutectic solvent (the highest solubility of theophylline was found in the NADES made of choline chloride and glycerol, which is 80% higher than pure DMSO that is currently used as a solvent for the APls (replacing specific solvent used in an industrial process); page 5, first paragraph; page 15, fourth paragraph).
Regarding claim 14, Cysewski discloses the method of claim 13, and further discloses wherein the artificial intelligence algorithm comprises one or more members selected from the group consisting of a linear regression algorithm, a logistic regression algorithm, a decision tree algorithm, a SVM algorithm, a Naive Bayes algorithm, a KNN algorithm, a K-means algorithm, a Random Forest algorithm (optional), a complex neural network algorithm (modeling was performed using ensemble of artificial neural networks, wherein algorithms utilizing multilayer perceptions were used during the machine learning phase (complex neural network algorithm); page 15, second paragraph), a support-vector machine algorithm, a gradient boosting algorithm, a DBSCAN algorithm, a dimensionally reduction algorithm, a gradient boosting algorithm, an AdaBoosting algorithm, and combinations thereof (optional).
Regarding claim 15, Cysewski discloses the method of claim 14, and further discloses wherein the algorithm comprises a Random Forest algorithm or a complex neural network algorithm (modeling was performed using ensemble of artificial neural networks, wherein algorithms utilizing multilayer perceptions were used during the machine learning phase (complex neural network algorithm); page 15, second paragraph).
Regarding claim 17, Cysewski discloses the method of claim 12, and further discloses wherein the method further comprises mixing the solvents and randomizing molar ratios of the solvents (NADES were used in their neat form and as cosolvents mixed with water in different molar ratios (randomizing molar ratios of the solvents); page 12, third paragraph). Regarding claim 18, Cysewski discloses the method of claim 12, and further discloses wherein an accuracy of the method of identifying the ideal solvent mix is at least about 80% (for SANN algorithm, the accuracy of the obtain.ad model shows 4 outliers out of 160 binary and ternary NADES samples tested (97.5 % accuracy); page 8, first paragraph; page 9, first paragraph, figure 6, top).
Regarding claim 19, Cysewski discloses the method of claim 12, and further discloses wherein the method comprising a training step and a validation step (the dataset was split into training (70%), test (15%) and validation (15%) subsets; page 9, figure 6, top; page 15, second paragraph).
Regarding claim 20, Cysewski discloses the method of claim 19, and further discloses wherein the validation step further comprises performing experiments to determine the ideal solvent mix (experimentally determining solubility of theophylline in the four most effective NADES and their mixtures with water; page 5, first paragraph; page 6, figure 4).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over 'Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning' to CYSEWSKI, et al. (hereinafter 'Cysewski')(IDS record filed on 04/30/2025) in view of 'Estimating the density of deep eutectic solvents applying supervised machine learning techniques' to ABDOLLAHZADEH, et al. (hereinafter 'Abdollahzadeh') (IDS record filed on 04/30/2025).
Regarding claim 16, Cysewski fails to disclose wherein the data comprises one or more physicochemical properties selected from the group consisting of pH, viscosity, conductivity, density, polarity, refractive index, color, odor, antioxidant capacity, and antimicrobial capacity.
Abdollahzadeh teaches wherein the data comprises one or more physicochemical properties selected from the group consisting of pH, viscosity, conductivity, density, polarity, refractive index, color, odor, antioxidant capacity, and antimicrobial capacity (pages 1-3). Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Abdollahzadeh with the teaching of Cysewski in order to provide a method for estimating the density of deep eutectic solvents applying supervised machine learning techniques (Abdollahzadeh, abstract).
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yan et al. (CN 114674703 A) disclose a coal direct liquefaction solvent hydrogen solubility prediction method, device and device. The method comprises: the physical property parameter of the first model compound in the direct liquefaction solvent acquire coal; measuring solubility test value of hydrogen in the first model compound through reaction kettle the physical parameter of the first model compound is input to the initial solubility prediction the model, the solubility prediction obtain of the hydrogen in the first model compound; according to the solubility test value and solubility prediction value, training the initial solubility prediction obtain, the target solubility prediction using the target solubility prediction model to carry out hydrogen solubility prediction In this way, the solubility prediction model can be adjusted continuously, so obtain to accurately prediction solubility prediction solubility of hydrogen in different model compounds of different coal direct liquefaction solvent, so as to acquire rule of the hydrogen in different coal direct liquefaction solvent.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857
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